CIF: Small: Statistical Learning Methods for Communications, Sensing and Control in Actuated Wireless Networks

CIF：小型：驱动无线网络中通信、传感和控制的统计学习方法

基本信息

批准号：
2008927
负责人：
Urbashi Mitra
金额：
$ 16.5万
依托单位：
University of Southern California
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2021-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008927&HistoricalAwards=false
关键词：
CIF Small Statistical Learning Methods

项目摘要

As the development of unmanned aerial vehicles (UAVs) or drones advances, there is potential for such aerial vehicles to be used to enhance and extend the cellular wireless communication infrastructure. As humans, we already engage in decisions about communication and actuation when using wireless communications. If we feel our connection is bad, we move in the hopes of experiencing a better wireless channel between our cellular phone and a base station. Wireless communications suffer from several deleterious propagation effects such as random fluctuations in signal strength, signal blockage due to obstructions and multipath effects which are due to signals bouncing off of buildings and other large objects. A mobile base station could serve to mitigate these effects by moving where additional coverage is needed, including the tracking of mobile hotspots (where there is extra traffic or congestion). To achieve this UAV enhanced communications infrastructure, this year-long project seeks to answer some key questions about cooperative and decentralized estimation as well as localization. In this exploratory project, a collection of UAVs is envisioned to work in concert. To this end, both centralized and distributed methods for estimation among the UAVs will be considered. Building on prior work on observation driven scheduling, the case of unknown underlying distributions of the sensed signal will be considered. In particular, data driven methods which enable the implicit learning of those distributions in a decentralized fashion will be developed. Observation driven scheduling will also be fully decentralized and different consensus-based strategies will be theoretically analyzed in terms of their performance. Tensor factorization-based non-parametric localization methods will be made more practical. In particular, a method for determining the number of targets in a field to be localized will be developed; prior work assumed that this number was known. Channel estimation based on feature maps and the non-parametric tensor factorization methods will be developed. A performance analysis of feature map-based methods will conducted.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着无人驾驶汽车（UAV）或无人机的发展，此类航空车有可能用于增强和扩展蜂窝无线通信基础设施。作为人类，我们已经在使用无线通信时已经进行了有关沟通和驱动的决定。如果我们觉得自己的连接不好，我们希望在我们的手机和基站之间体验更好的无线通道。无线通信遭受了几种有害的传播效应，例如信号强度中的随机波动，障碍物和多径效应引起的信号阻塞，这是由于信号从建筑物和其他大物体中反弹而引起的。移动基站可以通过移动需要额外覆盖范围的地方（包括移动热点（其中有额外的流量或拥塞）的跟踪）来减轻这些效果。为了实现这一无人机增强的通信基础设施，今年为期一年的项目试图回答有关合作和分散估计以及本地化的一些关键问题。在这个探索性项目中，设想一组无人机的集合可以共同努力。为此，将考虑将无人机之间的集中式和分布式方法进行估算。在先前的观察驱动计划的工作的基础上，将考虑感应信号的基础分布未知的情况。特别是，将开发以分散方式对这些分布进行隐式学习的数据驱动方法。观察驱动的调度也将完全分散，理论上将根据其绩效来分析不同的共识策略。基于张量分解的非参数定位方法将变得更加实用。特别是，将开发一种确定要本地化目标目标数量的方法；先前的工作假定该数字已知。将开发基于特征图和非参数张量分解方法的通道估计。将对基于特征地图的方法进行绩效分析。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。